1. First Technical Workshop on Standards for Hazard Monitoring, Data, Metadata and Analysis to Support Risk Assessment
June 2013
WMO, Geneva
Laurence McLean
Centre for Research on the Epidemiology of Disasters – CRED
Université Catholique de Louvain, Brussels, Belgium

2. State of Play Problems: Strong need for data:
Disaster events increasing in magnitude & frequency
Continuous need for disaster data collection
Important tool for a variety of institutions and organisations
Problems:
Lack of international consensus on best practice in collecting data
Variability in definitions, terminology, sources, etc.
1) The last three decades have seen an increase in both the frequency and destructiveness of
Natural hazards– many reasons why, higher exposure to hazard risk, influence of climate change– but key to this is the improvements in the reporting of disasters and their impacts. Very few slip through the radar.
2) Need to systematically maintain disaster data collection. Because… without understanding how extreme climate events (use this phrase) has affected people in the past, it is difficult to foresee the future. With no past data, entire model can depend on assumptions only.
3) Data and statistics is a vital resource underpinning in charge of relief and recovery, wider Disaster Risk Management strategy helping to build disaster resilience.
 You cannot effectively manage what you cannot measure.
Key problem: lack of agreement on method, variability in definitions, terminology, sources, etc. …)
Other considerations: time constraints, funding, etc

3. OVERVIEW OF EXISTING DATABASES
Global scale: 3 EM-DAT/CRED, NATCAT/MünichRe, SIGMA/SwissRe.
(+/-50)
Events Databases (i.e. DFO/Floods; USGS)
National and sub-national databases
• Global databases which measure human and economic impacts at country level.
• Able to provide infra country information, especially useful where there is variation in impact at the national level.
• Hazard specific databases e.g. USGS would focus on earthquake data.

4. EM-DAT/CRED Importance of measuring human impacts
Emergency Events Database, created 1988, maintained by CRED, Université Catholique de Louvain, Brussels
Project funded by OFDA/USAID, USA
Objective: Provide evidence-base to humanitarian and development actors at national and international levels
Publicly available (
Human impacts change over time over type of disaster. If those are not captured systematically then it is very difficult to convince decision makers on strengthening forecasting and preparedness systems
That is why CRED was established 20 years ago by epidemiologists and medical doctors who were concerned with the need for systematic reporting of the human impacts of natural disasters. It systemically takes data from over 184 countries worldwide, since 1900.
Risk models can be theoretically interesting but can be operationally inaccurate if past empirical data is not considered.
Temporally a rich base to draw upon
Publically available (raw data also available if justification is made why you need it)

5. Total occurrence of natural disasters from 1950 to 2012*
Source: CRED

6. Total number affected by natural disasters from 1900 to 2012.
97% are affected by climo/metero or hydrological cause.

7. EM-DAT DISASTER DEFINITION
“A situation or event which overwhelms local capacity, necessitating a request to a national or international level for external assistance; an unforeseen and often sudden event that causes great damage, destruction and human suffering”

8. EM-DAT Criteria 10 or more people reported killed and/or
100 or more people reported affected
Call for international assistance /state of emergency is declared

9. Sources of information
Multi source policy, comprising of UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies.
Priority is given to data from:
1) UN agencies
2) OFDA
3) Governments
4) International Federation (IFRC)
5) Insurance/reinsurance companies
6) Research Institutions
7) Press
Why this order?
This prioritization is not a reflection of the quality or value of the data but the recognition that most reporting sources do not cover all disasters or may have political limitations that could affect the figures.

10. Classification of Natural Disasters*
Biological disasters : Insect infestations, epidemics and animal attacks;
Geophysical disasters : Earthquakes and tsunamis, volcanic eruptions, dry mass movements (avalanches, landslides, rockfalls and subsidences of geophysical origin)
Climatological disasters : Droughts (with associated food insecurities), extreme temperatures and wildfires;
Hydrological disasters : Floods (including waves and surges), wet mass movements (avalanches, landslides, rockfalls and subsidences of hydrological origin);
Meteorological disasters : Storms (divided into 9 sub-categories).
*BELOW R., WIRTZ A., GUHA-SAPIR D. (2009). Disaster Category Classification and Peril Terminology for Operational Purposes, CRED: Brussels; MunichRe: Münich

11. EM-DAT/INDICATORS* Human indicators
Deaths : Persons confirmed as dead and persons missing and presumed dead (official figures when available)
Affected: People requiring immediate assistance during a period of emergency; it can also include displaced or evacuated people
Injured: People suffering from physical injuries, trauma or an illness requiring medical treatment as a direct result of a disaster.
Homeless: People needing immediate assistance for shelter.
Total affected: Sum of injured, homeless, and affected.
* CRED currently working on the harmonization of human and economic indicators (in collaboration with MünichRe ,SwissRe, UNDP, …)
Impact data can be fluid, so revisions may necessary to accurately reflect complex situations
Need to be attuned to these statistical dynamics to give most accurate picture.

12. EM-DAT/INDICATORS*
Economic impact
Direct economic losses: Given by sector (when available) and expressed in US$ (at the time the disaster happens)
Insured losses and reconstruction cost
* CRED currently working on the harmonization of human and economic indicators (in collaboration with MünichRe ,SwissRe, UNDP, …)

13. comparable over time and space
EMDAT CREDibility
Consistent methodology, Rules and definition
Validation methods and tools
Over 20 years experience in data collection and management
Transparency and automatisation (data entry and ouput)
EMDAT uses clear and transparent inclusion criteria which has not changed ever since it began. This means comparability across time and across countries remain constant.
Our ethos is producing quality data, so we do not use real time data. We triangulate sources.
EMDAT is systematic, ALL countries members of the UN and all natural disasters are included.
The result: data is
comparable over time and space

14. WEAKNESSES OF EM-DAT Global database
Limited potential for analysis of disaster occurrence and impact on smaller, intra-country spatial scale.
Public usage of EM-DAT may lead to inappropriate use
Only at the global scale, limited the potential capturing occurrence + impact complexities

15. What is the challenge?
Lack of clear standardised collection methodologies and definitions.
Inconsistencies, data gaps, poor inter-operability etc.
Confusion in evaluating disaster situations and causes obstacles for disaster prevention and preparedness.
At the mercy of other systems of data collection in relation to sources and other databases. Bad data can lead ineffective management

16. Quality of Disaster Databases as the reporting systems which feed them
Quality of Disaster Databases
can only be as good
as the reporting systems which feed them
The hospital dataset is used for the first three Objectives

17. Successful and sustainable databases – what is the criteria?
Clear and limited scope (i.e. variables)
Best environment (academic vs gov. administration)
Scientifically sound methodologies and definitions
What can be done to improve collection?
Limited to maintain relevancy and avoid data gaps and mistakes, and empty variables
Setting is very important to create and maintain a good disaster database
collaborative research environment, inherently sustainable, is best placed to deliver scientific rigour in data collection and analysis
Sustainability – Research institutions represent stable environments characterised by long-term engagement, hosting a ‘captive’ audience of scientists and researchers with sound technical and theoretical knowledge of natural disasters, compared with administrative settings less susceptible to change.
Credibility – Independent scientific research may have higher credibility compared with other sources.
Expertise/Quality – Researchers and practitioners have experience, skills, technical ability and stake in the quality of statistics they produce.

18. Next steps forward for EM-DAT disaster database
85% of natural disaster related deaths occur in Asian
In 2010, CRED undertook a study of disaster collection efforts in six Asian countries
Aim of to strengthen national structures
(Nepal Indonesia, Sri Lanka, Vietnam, Philippines and Bangladesh)
The goal was to strengthen methods, structures and operations, as well as improve visibility, accessibility, interoperability and applicability of disaster databases at national levels in nationally based technical institutes.

19. Decentralisation of disaster data collection…why?
Local sources are better positioned to reflect the local picture
Downscaling to national and sub national level can lead to improved data quality
Potential for project sustainability as partner institutions take ownership and responsibility
Overall aim: To set up a concentric system of disaster impact data collection – interoperable between sub-national, national, regional and global levels – using a harmonised set definitions and methods.
Through this CRED hopes to develop NEM-DAT for Asia.
In focusing on the national and sub national level collection, data capture will be enhanced because of greater accessibility and better linkages with local authorities and field partners. For example, hazard risk assessments can become geographically bespoke, increasing relevancy and data precision.
Researchers and practitioners have experience, skills, technical ability and stake in the quality of statistics they produce

20. Inter-operability system
Sub-National
National
Regional
Global
The ideal…Interoperable system based on standardised methods and definitions
We invite collaboration and co-operation on this with national meteo agencies.

21. Laurence McLean (CRED) Laurence.mclean@uclouvain.be
Thank you …
Laurence McLean (CRED)

22. Methodological Issues
What has improved?
Information systems in the last 30 years
Availability of statistical data
Better Quality of Data
What is still lacking?
No systematic and standardized data collection for historical data
Standards and definitions
The hospital dataset is used for the first three Objectives

23. Key Problems  
Lack of standardized collection methodologies and definitions, ambiguities exist regarding:
1. Intent behind data reporting:
i.e. Data not gathered for statistical purposes
2. Dates reported
i.e. Long-term disaster (Drought/Famine)
3. Loss definition of human impact
i.e. Definition of affected is open to different interpretation
4. Evaluation methods of damages
i.e. Poor reporting, no internationaly accepted methods
5. Geographical location
i.e. Change in national boundaries

24. Improving the completeness of information (disaggregated data)
Challenges
Improving the completeness of information (disaggregated data)
Limitations :
Comparability and inter-operability
Data collection initiatives project-based (lack of long-term perspective)
Lack of comprehensive policy among organizations

25. WHAT ARE THE NEEDS? Adequate database structures
Standardized methodology and operational approaches
Interoperability
Sustainability

26. STRENGHTS OF EM-DAT Unique free accessible database
Acts as a reference point for global analysis of disaster occurrence and impact
Unique basis for policy paper on disaster reduction and risks
International recognition and CREDibility
Capacity to provide methods and guidelines